About the survey and study population

The DHS survey program (www.measuredhs.com) has been operating in Bangladesh since 1993. The present study was based on data collected from the sixth wave of Bangladesh Demographic and Health Survey (BDHS) carried out in 2011. The survey is cross-sectional in nature and the sample population is nationally representative. The survey was conducted under the authority of the National Institute of Population Research and Training (NIPORT) and was implemented by Mitra and Associates, a well-known research institution in the country. Technical and financial assistance were provided by ICF International of Calverton (Maryland, USA) and United States Agency for International Development (USAID) respectively [13].

Bangladesh is the eighth most populous country in the world and third most populous in South Asia. It is also the most densely populated country in the world (excluding the city-states) with about 1015 inhabitants/km² [13]. Due to rapid population growth, family planning policies to curb fertility rates have been in place throughout the country since its independence in 1971. The country is divided into seven administrative divisions (Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, and Sylhet) and survey was carried out in all these divisions encompassing both rural and urban population. The details of survey design, sampling method, data collection and distribution have already been described elsewhere [13, 19]. Briefly, the survey employed a two-stage stratified sampling of the households in a systematic way to ensure that the sample is nationally representative. In total 18,222 ever married women ageing between 12 and 49 years were selected for interview, of whom 17,842 were finally surveyed (response rate of 98%). The principle objectives of the survey were to provide most recent scenario of demographic (e.g. fertility, infant & maternal mortality rates), socioeconomic (e.g. literacy, employment, wealth status, food security), health indicators (e.g. malnutrition, rates of contraceptive use, skilled birth assistance, health literacy).

Variable selection

The primary outcome variable of this study was pregnancy intention status among women for their last pregnancy. Though it is recognised among researchers that the concept of unintended pregnancy is a complex and nuanced one [17], it generally includes pregnancies described as mistimed or unwanted, and is usually used as a binary outcome (intended/unintended or wanted/unwanted). In this study it was measured by the response to the question on pregnancy intendedness and was dichotomized followingly: intended = wanted then, and unintended = wanted later/wanted no more.

Unmet need for contraception was the main exposure variable in this study. It was measured based on contraception utilisation status among participants who did/did not report desire for spacing or limiting childbirth. Unmet need for contraception refers to non-utilization of contraception measures among women who are fecund and sexually active, not want any more pregnancies or want to delay the next pregnancy. The concept of unmet need points to the gap between women’s reproductive intentions and their contraceptive behaviour. Participants who wanted to space/limit childbirth but not using contraception were considered as having unmet need for contraception.

For the selection of relevant covariates, we conducted a literature search in prominent medical databases for studies on unintended pregnancy. Based on literature review, and availability in the BDHS dataset, the following variables were selected for this study: age (<25/≥25); type of residence (urban/rural); educational attainment of respondent: nil (no formal education), primary (1–5 years of formal education); secondary/higher (>5 years of formal education); educational attainment of husband nil (no formal education); primary (1–5 years of formal education); secondary/higher (>5 years of formal education). husband’s occupation: blue collar (includes employments in farming, construction; white collar (includes employments in service, teaching, health sector, business); employment (yes/no); sex of household head (male/female); age at first birth (<18/≥18); ideal number of children (1/>1); has a say in own healthcare decision (yes/no); ever had a terminated pregnancy(yes/no); currently using any contraception (yes/no); unmet need for contraception(yes/no); decision maker for using contraception (yes/no); household wealth status (poor/middle/rich). DHS studies employs principal component analysis method to measure wealth index of households based on ownership of household assets e.g. durable goods (television, bicycle), household characteristics (sanitation facilities, construction materials) [13]. The factor scores are summed and standardized for each household which places them in a continuous scale based on relative wealth scores. Then the scores are categorized into quintiles where each households fall into a category, with the lowest scores representing the poorest and highest representing the richest households [19].

Data analysis

The BDHS dataset contain information on wide range of variables. The dataset was checked to include only those participants for whom all information necessary for the present study were available. Prior to analysis, data were weighted by sample weights to generate population estimates. Descriptive analysis (frequency distribution) was performed to show the basic characteristics (e.g. demographic, socioeconomic, contraceptive use) of the sample population. Pearson’s χ ² tests as well as cross tabulation were performed to show the group differences in terms of pregnancy intentions across the explanatory variables. At this stage, the explanatory variables were checked for multicollinearity to ensure validity for further analysis. Variables which showed statistically significant association (p < 0.05) in the χ ² tests were included in the multivariable regression analysis. Given the complex clustered nature of the survey, we employed generalised estimating equation (GEE) method which serves a reliable tool for dealing with clustered data [19, 20]. GEE is a commonly employed statistical approach to fit a marginal model for clustered data analysis in clinical trials and biomedical studies [20, 21]. Adjusted odds ratios (OR) with 95% confidence intervals (CI) were calculated to measure the strength of associations between pregnancy intention status and the response variables in the model. A two-tailed p-value of <0.05 was used to assess the statistical significance for all tests. All analyses were carried out with the Mac Version of IBM SPSS Statistics 21.

Results of multivariate analysis

Main findings

Based on the analysis of Bangladesh Demographic and Health Survey 2011 data, this cross-sectional study reports the prevalence of unmet need for contraception and unintended pregnancy in a nationally representative population in Bangladesh. Findings of the analysis indicate a suboptimal utilization of contraception as about one-third of the eligible women were found to be non-users any contraceptive measures. Although about two-fifth of the women reported having the authority to make healthcare decisions on their own, only 7.1% said that they were the decision makers for contraception. The rate of unintended pregnancy seemed to have stagnated as there was practically no improvement compared to the 2004 demographic and health survey findings. However, the rate is still below the global average of 38% (2013 estimate) [22]. The rate of unmet need for contraception was 13.1%, which is slightly higher than the global average of 12.3% (2010 estimate) [23]. Women who reported richest wealth status were significantly less likely to report unmet need for contraception and unintended pregnancies compared to women from poorer households. Studies conducted in other countries have demonstrated that poverty is strongly correlated with both unmet need for contraception and unintended pregnancy [24]. Clearly, women from poorer households are less likely to be able to spend for personal healthcare such as reproductive health services and more likely to experience unintended pregnancies and other pregnancy related complications. Results of the multivariate analysis revealed a remarkably strong association between unmet need for contraception and unintended pregnancy. The association was statistically significant in both the unadjusted and adjusted model, however the level of association appeared to be stronger after adjusting for the potential confounders.

Comparison with existing studies

In comparison with the findings from BDHS 2004, our result revealed that the rate of unintended pregnancy has remained virtually unchanged during this period (30% in 2004 vs 29.3% in 2011) [25, 26]. This rate is lower compared to the developing country average of 40% [27], but higher than neighbouring countries in South Asia [23, 28]. In line with previous evidences [29], our findings showed that both unmet need and non-use of contraception are positively associated with unintended pregnancy. However, unmet need showed the strongest association among all the predictive factors in the present study (Result not shown). Available evidence from previous studies suggests that non-use of whatever type (inadequate use of contraception, discontinuation, and contraceptive failure) is associated with increased likelihood of unintended pregnancy [30]. Moreover, as unintended pregnancy has been shown to be associated with increased risk of abortion and maternal morbidity [31], this remains a particular concern for women’s reproductive health in Bangladesh since induced abortion is not yet legalized and the scope of safe abortion is also limited, especially in the rural areas. In addition to unmet need, contraceptive discontinuation and non-use are also very common phenomenon in Bangladesh and contributes to unintended pregnancy [32].